import pandas as pd
import numpy as np
import seaborn as sns 
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix,accuracy_score, classification_report,r2_score
from sklearn.preprocessing import StandardScaler
import pandas_profiling as pp
from pandas_profiling import*
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')
import pickle

df=pd.read_excel("creditcarddefault.xlsx")
df

	ID	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_0	PAY_2	PAY_3	BILL_AMT1	BILL_AMT2	BILL_AMT3	PAY_AMT1	PAY_AMT2	PAY_AMT3	default.payment.next.month
0	1	20000	2	2	1	24	2	2	-1	3913	3102	689	0	689	0	1
1	2	120000	2	2	2	26	-1	2	0	2682	1725	2682	0	1000	1000	1
2	3	90000	2	2	2	34	0	0	0	29239	14027	13559	1518	1500	1000	0
3	4	50000	2	2	1	37	0	0	0	46990	48233	49291	2000	2019	1200	0
4	5	50000	1	2	1	57	-1	0	-1	8617	5670	35835	2000	36681	10000	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29995	29996	220000	1	3	1	39	0	0	0	188948	192815	208365	8500	20000	5003	0
29996	29997	150000	1	3	2	43	-1	-1	-1	1683	1828	3502	1837	3526	8998	0
29997	29998	30000	1	2	2	37	4	3	2	3565	3356	2758	0	0	22000	1
29998	29999	80000	1	3	1	41	1	-1	0	-1645	78379	76304	85900	3409	1178	1
29999	30000	50000	1	2	1	46	0	0	0	47929	48905	49764	2078	1800	1430	1

30000 rows × 16 columns

df.shape

(30000, 16)

# prof=ProfileReport(df)
# prof

df.columns

Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0',
       'PAY_2', 'PAY_3', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'PAY_AMT1',
       'PAY_AMT2', 'PAY_AMT3', 'default.payment.next.month'],
      dtype='object')

df.drop("ID",axis=1,inplace=True)
df

	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_0	PAY_2	PAY_3	BILL_AMT1	BILL_AMT2	BILL_AMT3	PAY_AMT1	PAY_AMT2	PAY_AMT3	default.payment.next.month
0	20000	2	2	1	24	2	2	-1	3913	3102	689	0	689	0	1
1	120000	2	2	2	26	-1	2	0	2682	1725	2682	0	1000	1000	1
2	90000	2	2	2	34	0	0	0	29239	14027	13559	1518	1500	1000	0
3	50000	2	2	1	37	0	0	0	46990	48233	49291	2000	2019	1200	0
4	50000	1	2	1	57	-1	0	-1	8617	5670	35835	2000	36681	10000	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29995	220000	1	3	1	39	0	0	0	188948	192815	208365	8500	20000	5003	0
29996	150000	1	3	2	43	-1	-1	-1	1683	1828	3502	1837	3526	8998	0
29997	30000	1	2	2	37	4	3	2	3565	3356	2758	0	0	22000	1
29998	80000	1	3	1	41	1	-1	0	-1645	78379	76304	85900	3409	1178	1
29999	50000	1	2	1	46	0	0	0	47929	48905	49764	2078	1800	1430	1

30000 rows × 15 columns

df.drop(df.iloc[:,15:23],inplace=True,axis=1)
df

	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_0	PAY_2	PAY_3	BILL_AMT1	BILL_AMT2	BILL_AMT3	PAY_AMT1	PAY_AMT2	PAY_AMT3	default.payment.next.month
0	20000	2	2	1	24	2	2	-1	3913	3102	689	0	689	0	1
1	120000	2	2	2	26	-1	2	0	2682	1725	2682	0	1000	1000	1
2	90000	2	2	2	34	0	0	0	29239	14027	13559	1518	1500	1000	0
3	50000	2	2	1	37	0	0	0	46990	48233	49291	2000	2019	1200	0
4	50000	1	2	1	57	-1	0	-1	8617	5670	35835	2000	36681	10000	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29995	220000	1	3	1	39	0	0	0	188948	192815	208365	8500	20000	5003	0
29996	150000	1	3	2	43	-1	-1	-1	1683	1828	3502	1837	3526	8998	0
29997	30000	1	2	2	37	4	3	2	3565	3356	2758	0	0	22000	1
29998	80000	1	3	1	41	1	-1	0	-1645	78379	76304	85900	3409	1178	1
29999	50000	1	2	1	46	0	0	0	47929	48905	49764	2078	1800	1430	1

30000 rows × 15 columns

df.isnull().sum()

LIMIT_BAL                     0
SEX                           0
EDUCATION                     0
MARRIAGE                      0
AGE                           0
PAY_0                         0
PAY_2                         0
PAY_3                         0
BILL_AMT1                     0
BILL_AMT2                     0
BILL_AMT3                     0
PAY_AMT1                      0
PAY_AMT2                      0
PAY_AMT3                      0
default.payment.next.month    0
dtype: int64

df['default.payment.next.month'].value_counts()

0    23364
1     6636
Name: default.payment.next.month, dtype: int64

%matplotlib inline
df['default.payment.next.month'].value_counts(normalize=True).plot.bar()
plt.title('Non Defaulters vs Defaulters')
plt.show()

df.nunique()

LIMIT_BAL                        81
SEX                               2
EDUCATION                         7
MARRIAGE                          4
AGE                              56
PAY_0                            11
PAY_2                            11
PAY_3                            11
BILL_AMT1                     22723
BILL_AMT2                     22346
BILL_AMT3                     22026
PAY_AMT1                       7943
PAY_AMT2                       7899
PAY_AMT3                       7518
default.payment.next.month        2
dtype: int64

df[['SEX','EDUCATION',"MARRIAGE"]].describe()

	SEX	EDUCATION	MARRIAGE
count	30000.000000	30000.000000	30000.000000
mean	1.603733	1.853133	1.551867
std	0.489129	0.790349	0.521970
min	1.000000	0.000000	0.000000
25%	1.000000	1.000000	1.000000
50%	2.000000	2.000000	2.000000
75%	2.000000	2.000000	2.000000
max	2.000000	6.000000	3.000000

df['MARRIAGE']=df['MARRIAGE'].replace({0:3})

df['EDUCATION']=df['EDUCATION'].replace({0:4,5:4,6:4})

def plot_categoric(cat_col):
    plt.title('{0} vs DEFAULT PROBABILITY '.format(cat_col))
    df.groupby(cat_col)['default.payment.next.month'].mean().plot.bar()
    plt.show

%matplotlib inline
plot_categoric('SEX')

plot_categoric('MARRIAGE')

plot_categoric('EDUCATION')

Limit Balance and influence on target var

sns.distplot(df['LIMIT_BAL'])
plt.title('DISTN OF LIMIT_BAL')
plt.show()

df.groupby('default.payment.next.month')['LIMIT_BAL'].mean().plot.bar()
plt.title('Mean Limit Balance v/s Target')
plt.show()

Correlation And Heatmap

corr=df.corr()

plt.figure(figsize=(18,15))
sns.heatmap(corr,annot=True ,vmin=-1,cmap="Paired")
plt.show()

x=df.iloc[:,0:14]
x

	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_0	PAY_2	PAY_3	BILL_AMT1	BILL_AMT2	BILL_AMT3	PAY_AMT1	PAY_AMT2	PAY_AMT3
0	20000	2	2	1	24	2	2	-1	3913	3102	689	0	689	0
1	120000	2	2	2	26	-1	2	0	2682	1725	2682	0	1000	1000
2	90000	2	2	2	34	0	0	0	29239	14027	13559	1518	1500	1000
3	50000	2	2	1	37	0	0	0	46990	48233	49291	2000	2019	1200
4	50000	1	2	1	57	-1	0	-1	8617	5670	35835	2000	36681	10000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29995	220000	1	3	1	39	0	0	0	188948	192815	208365	8500	20000	5003
29996	150000	1	3	2	43	-1	-1	-1	1683	1828	3502	1837	3526	8998
29997	30000	1	2	2	37	4	3	2	3565	3356	2758	0	0	22000
29998	80000	1	3	1	41	1	-1	0	-1645	78379	76304	85900	3409	1178
29999	50000	1	2	1	46	0	0	0	47929	48905	49764	2078	1800	1430

30000 rows × 14 columns

y=df.iloc[:,-1]
y

0        1
1        1
2        0
3        0
4        0
        ..
29995    0
29996    0
29997    1
29998    1
29999    1
Name: default.payment.next.month, Length: 30000, dtype: int64

Train and test

train_x,test_x,train_y,test_y=train_test_split(x,y,test_size=0.3,random_state=2)

LogisticRegreesion

model=LogisticRegression()

model.fit(train_x,train_y)

LogisticRegression()

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predict_y=model.predict(test_x)

accuracy_score(test_y,predict_y)

0.7782222222222223

# accuracy_score(predict_y,test_y)

model.score(train_x,train_y)

0.7788571428571428

model.score(test_x,test_y)

0.7782222222222223

diff_log_re=pd.DataFrame({'Actual':test_y , 'Predicted':predict_y})
diff_log_re

	Actual	Predicted
7945	1	0
16536	0	0
26726	0	0
16333	1	0
20629	0	0
...	...	...
15968	0	0
27941	0	0
17066	0	0
2178	0	0
24758	0	0

9000 rows × 2 columns

DecisionTree

DT = DecisionTreeClassifier()

DT.fit(train_x,train_y)

DecisionTreeClassifier()

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pred_y2 = DT.predict(test_x)

print("Accuracy =",accuracy_score(test_y,pred_y2))
print("Training Score =",DT.score(train_x,train_y))

Accuracy = 0.7187777777777777
Training Score = 0.9994761904761905

diff_dtr=pd.DataFrame({'Actual':test_y , 'Predicted':pred_y2})
diff_dtr

	Actual	Predicted
7945	1	0
16536	0	0
26726	0	1
16333	1	0
20629	0	0
...	...	...
15968	0	0
27941	0	0
17066	0	0
2178	0	0
24758	0	1

9000 rows × 2 columns

RandomForest

RF = RandomForestClassifier()

RF.fit(train_x,train_y)

RandomForestClassifier()

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pred_y3 = RF.predict(test_x)

accuracy_score(test_y,pred_y3)

0.8128888888888889

RF.score(train_x,train_y)

0.9994761904761905

RF.score(test_x,test_y)

0.8128888888888889

confusion_matrix1 = confusion_matrix(test_y, pred_y3)
print(confusion_matrix1)

[[6585  421]
 [1263  731]]

diff_rf=pd.DataFrame({'Actual':test_y , 'Predicted':pred_y3})
diff_rf.head(10)

	Actual	Predicted
7945	1	0
16536	0	0
26726	0	0
16333	1	0
20629	0	0
321	0	0
9579	0	0
27004	0	0
2360	0	0
6593	0	0

with open('RF.pickle','wb') as f:
    pickle.dump(RF,f)

with open ('RF.pickle','rb') as f:
    model=pickle.load(f)

input = np.array([[70000,1,2,2,30,1,2,2,65802,67369,65701,3200,0,3000]])
x=RF.predict(input)
print(x)

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